Secure Collaborative Spectrum Sensing in the Presence of Primary User Emulation Attack in Cognitive Radio Networks

Document Type : Research Article

Authors

1 PhD. Student, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

2 MSc. Student, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

3 Associate Professor, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

Abstract

Collaborative Spectrum Sensing (CSS) is an effective approach to improve the detection performance in Cognitive Radio (CR) networks. Inherent characteristics of the CR have imposed some additional security threats to the networks. One of the common threats is Primary User Emulation Attack (PUEA). In PUEA, some malicious users try to imitate primary signal characteristics and defraud the CR users to prevent them from accessing the idle frequency bands. The present study investigates a new CSS scheme in the presence of a smart PUEA, which is aware of idle frequency channels and transmits its fake signal in a way that CR users are not easily able to discriminate between the received signal from the PU and PUEA. The idea is based on the Bayes risk criterion. More precisely, the sensing results of the CR users are summed up in the Fusion Center (FC) and compared with the optimum threshold that minimizes the Bayes risk. We also discuss practical limitation issue that need to be considered when applying the proposed method. Simulation results are provided to indicate the superiority of the proposed method against PUEA compared with conventional method.

Keywords

References

[1]Mitola J, Maguire GQ. Cognitive radio: making software radios more personal. IEEE Personal Communication 1999; 6(4): 13-18.
[2]Akyildiz IF, Lee WY, Vuran MC, Mohanty S. NeXt generation/dynamic spectrum access cognitive radio wireless networks: A survey. Computer Networks 2006; 50(13): 2127-2159.
[3]Mishra SM, Sahai A, Brodersen RW. Cooperative sensing among cognitive radios. In Proceedings of the IEEE International Conference on Communications 2006; 1658-1663.
[4]R. Chen, J. Park, Y. Hou, and J. Reed, “Toward secure distributed spectrum sensing in cognitive radio networks,” IEEE Commun. Mag., vol. 46, no. 4, pp. 50–55, Apr. 2008
[5]Anand S, Jin Z, Subbalakshmi K. An analytical model for primary user emulation attacks in cognitive radio networks. In Proceeding IEEE International Dynamic Spectrum Access Networks 2008; 1-6.
[6]Jin Z, Subbalakshmi k. Detecting Primary User Emulation Attacks in Dynamic Spectrum Access Networks. IEEE International Conference on Communications 2009; 1–5.
[7]Chen C, Cheng H, Yao Y-D. Cooperative spectrum sensing in cognitive radio networks in the presence of the primary user emulation attack. IEEE Transactions on Wireless Communications 2011; 10(7): 2135-2141.
[8]Haghighat M, Sadough SMS. Cooperative spectrum sensing for cognitive radio networks in the presence of smart malicious users. International Journal of Electronics and Communications (AUE) 2014; 68(6): 520-527.
[9]Haghighat M, Sadough SMS. Smart primary user emulation in cognitive radio networks: defense strategies against radio-aware attacks and robust spectrum sensing. Transactions on Emerging Telecommunications Technologies 2014.
[10]Saber MJ, Sadough SMS. Optimisation of cooperative spectrum sensing for cognitive radio networks in the presence of smart primary user emulation attack. Transactions on Emerging Telecommunications Technologies 2014.
[11]Digham F, Alouini M, Simon M. On the energy detection of unknown signals over fading channels. In Proceedings of IEEE International Conference on Communications 2003; 5: 3575–
3579.
[12]Ma J, Zhao G, Li Y. Soft combination and
detection for cooperative spectrum sensing in cognitive radio networks. IEEE Transactions on Wireless Communications 2008; 7(11): 4502-4507.
[13]Varshney PK. Distributed detection and data fusion. Springer-Verlag 1997.

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